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The number of Android malware variants (clones) are on the rise and, to stop this attack of clones we need to develop new methods and techniques for analysing and detecting them. As a first step, we need to study how these malware clones are generated. This will help us better anticipate and recognize these clones. In this paper we present a new tool named DroidMorph, that provides morphing of Android applications (APKs) at different level of abstractions, and can be used to create Android application (malware/benign) clones. As a case study we perform testing and evaluating resilience of current commercial anti-malware products against attack of the Android malware clones generated by DroidMorph. We found that 8 out of 17 leading commercial anti-malware programs were not able to detect any of the morphed APKs. We hope that DroidMorph will be used in future research, to improve Android malware clones analysis and detection, and help stop them.
Machine learning (ML) classifiers are vulnerable to adversarial examples. An adversarial example is an input sample which is slightly modified to induce misclassification in an ML classifier. In this work, we investigate white-box and grey-box evasio
According to the Symantec and F-Secure threat reports, mobile malware development in 2013 and 2014 has continued to focus almost exclusively ~99% on the Android platform. Malware writers are applying stealthy mutations (obfuscations) to create malwar
Android malware has been on the rise in recent years due to the increasing popularity of Android and the proliferation of third party application markets. Emerging Android malware families are increasingly adopting sophisticated detection avoidance t
We present BPFroid -- a novel dynamic analysis framework for Android that uses the eBPF technology of the Linux kernel to continuously monitor events of user applications running on a real device. The monitored events are collected from different com
With the growth of mobile devices and applications, the number of malicious software, or malware, is rapidly increasing in recent years, which calls for the development of advanced and effective malware detection approaches. Traditional methods such